DyMRL: Dynamic Multispace Representation Learning for Multimodal Event Forecasting in Knowledge Graph

• URL: http://arxiv.org/abs/2603.24636v1
• Date: Wed, 25 Mar 2026 09:45:16 GMT
• Title: DyMRL: Dynamic Multispace Representation Learning for Multimodal Event Forecasting in Knowledge Graph
• Abstract summary: We propose DyMRL, a Dynamic Multispace Representation Learning approach to efficiently acquire and fuse multimodal temporal knowledge.<n>We show that DyMRL outperforms state-of-the-art dynamic unimodal and static multimodal baseline methods.
• Score: 15.450478774839324
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate representation of multimodal knowledge is crucial for event forecasting in real-world scenarios. However, existing studies have largely focused on static settings, overlooking the dynamic acquisition and fusion of multimodal knowledge. 1) At the knowledge acquisition level, how to learn time-sensitive information of different modalities, especially the dynamic structural modality. Existing dynamic learning methods are often limited to shallow structures across heterogeneous spaces or simple unispaces, making it difficult to capture deep relation-aware geometric features. 2) At the knowledge fusion level, how to learn evolving multimodal fusion features. Existing knowledge fusion methods based on static coattention struggle to capture the varying historical contributions of different modalities to future events. To this end, we propose DyMRL, a Dynamic Multispace Representation Learning approach to efficiently acquire and fuse multimodal temporal knowledge. 1) For the former issue, DyMRL integrates time-specific structural features from Euclidean, hyperbolic, and complex spaces into a relational message-passing framework to learn deep representations, reflecting human intelligences in associative thinking, high-order abstracting, and logical reasoning. Pretrained models endow DyMRL with time-sensitive visual and linguistic intelligences. 2) For the latter concern, DyMRL incorporates advanced dual fusion-evolution attention mechanisms that assign dynamic learning emphases equally to different modalities at different timestamps in a symmetric manner. To evaluate DyMRL's event forecasting performance through leveraging its learned multimodal temporal knowledge in history, we construct four multimodal temporal knowledge graph benchmarks. Extensive experiments demonstrate that DyMRL outperforms state-of-the-art dynamic unimodal and static multimodal baseline methods.

Related papers

• CL-Anomaly: Layer-Adaptive Mixture-of-Experts with Multimodal Large Language Model for Continual Learning in Anomaly Detection [33.94199994115719]
  CL-Anomaly is a parameter-efficient fine-tuning framework for continuous learning for anomaly detection.<n>PrivLoRA physically isolates task-specific subspaces in the parameter space to prevent semantic entanglement of anomaly knowledge.<n> Layer-Adaptive Shared Experts maintain cross-task representations within a unified feature space.
  arXiv  Detail & Related papers  (2026-07-03T03:53:58Z)
• POINTS-Seeker: Towards Training a Multimodal Agentic Search Model from Scratch [84.73366911912512]
  We introduce Agentic Seeding, a dedicated phase designed to weave the precursors necessary for eliciting agentic behaviors.<n>We propose V-Fold, an adaptive history-aware compression scheme that preserves recent dialogue turns in high fidelity while folding historical context into the visual space via rendering.<n>We develop POINTS-Seeker-8B, a state-of-the-art multimodal agentic search model that consistently outperforms existing models across six diverse benchmarks.
  arXiv  Detail & Related papers  (2026-04-15T16:09:37Z)
• Exploring Modality-Aware Fusion and Decoupled Temporal Propagation for Multi-Modal Object Tracking [14.177367335305627]
  We propose MDTrack, a novel framework for modality aware fusion and decoupled temporal propagation in multimodal object tracking.<n>For modality aware fusion, we allocate dedicated experts to each modality, including infrared, event, depth, and RGB, to process their respective representations.<n>For decoupled temporal propagation, we introduce two separate State Space Model structures to independently store and update the hidden states of the RGB and X modal streams.
  arXiv  Detail & Related papers  (2026-03-10T07:10:05Z)
• Learning Multi-Modal Mobility Dynamics for Generalized Next Location Recommendation [51.00494428978262]
  We leverage multi-modal spatial-temporal knowledge to characterize mobility dynamics for the location recommendation task.<n>First, we construct a unified spatial-temporal relational graph (STRG) for multi-modal representation.<n>Second, we design a gating mechanism to fuse spatial-temporal graph representations of different modalities.
  arXiv  Detail & Related papers  (2025-12-27T14:23:04Z)
• MMhops-R1: Multimodal Multi-hop Reasoning [89.68086555694084]
  We introduce MMhops, a novel benchmark designed to evaluate and foster multi-modal multi-hop reasoning.<n> MMhops dataset comprises two challenging task formats, Bridging and Comparison.<n>We propose MMhops-R1, a novel multi-modal Retrieval-Augmented Generation framework for dynamic reasoning.
  arXiv  Detail & Related papers  (2025-12-15T17:29:02Z)
• Continual Multimodal Contrastive Learning [99.53621521696051]
  Multimodal Contrastive Learning (MCL) advances in aligning different modalities and generating multimodal representations in a joint space.<n>However, a critical yet often overlooked challenge remains: multimodal data is rarely collected in a single process, and training from scratch is computationally expensive.<n>In this paper, we formulate CMCL through two specialized principles of stability and plasticity.<n>We theoretically derive a novel optimization-based method, which projects updated gradients from dual sides onto subspaces where any gradient is prevented from interfering with the previously learned knowledge.
  arXiv  Detail & Related papers  (2025-03-19T07:57:08Z)
• RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
  Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
  arXiv  Detail & Related papers  (2024-10-18T03:45:19Z)
• Multi-Modality Spatio-Temporal Forecasting via Self-Supervised Learning [11.19088022423885]
  We propose a novel MoST learning framework via Self-Supervised Learning, namely MoSSL. Results on two real-world MoST datasets verify the superiority of our approach compared with the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-05-06T08:24:06Z)
• Unsupervised Learning of Hybrid Latent Dynamics: A Learn-to-Identify Framework [9.587766468221535]
  Modern applications increasingly require unsupervised learning of latent dynamics from high-dimensional time-series. This paper investigates the use of physics inductive bias specific to the data being modeled, and a learn-to-identify strategy that separates forecasting objectives from the data used for the identification. We combine these two strategies in a novel framework for unsupervised meta-learning of hybrid latent dynamics (Meta-HyLaD)
  arXiv  Detail & Related papers  (2024-03-13T02:33:57Z)
• Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives [56.2139730920855]
  We present a systematic analysis of MM-VUFMs specifically designed for road scenes. Our objective is to provide a comprehensive overview of common practices, referring to task-specific models, unified multi-modal models, unified multi-task models, and foundation model prompting techniques. We provide insights into key challenges and future trends, such as closed-loop driving systems, interpretability, embodied driving agents, and world models.
  arXiv  Detail & Related papers  (2024-02-05T12:47:09Z)
• Continual Multimodal Knowledge Graph Construction [62.77243705682985]
  Current Multimodal Knowledge Graph Construction (MKGC) models struggle with the real-world dynamism of continuously emerging entities and relations. This study introduces benchmarks aimed at fostering the development of the continual MKGC domain. We introduce MSPT framework, designed to surmount the shortcomings of existing MKGC approaches during multimedia data processing.
  arXiv  Detail & Related papers  (2023-05-15T14:58:28Z)
• Distilled Mid-Fusion Transformer Networks for Multi-Modal Human Activity Recognition [34.424960016807795]
  Multi-modal Human Activity Recognition could utilize the complementary information to build models that can generalize well. Deep learning methods have shown promising results, their potential in extracting salient multi-modal spatial-temporal features has not been fully explored. A knowledge distillation-based Multi-modal Mid-Fusion approach, DMFT, is proposed to conduct informative feature extraction and fusion to resolve the Multi-modal Human Activity Recognition task efficiently.
  arXiv  Detail & Related papers  (2023-05-05T19:26:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.